Field of the Invention: The present invention relates generally to systems for monitoring environmental conditions and, more particularly, to detection of environmental conditions in buildings that are conducive to fungal growth.
State of the Art: There is a growing concern about fungal contamination in both residential and commercial buildings. To address this concern, there is a need for a system that detects environmental conditions conducive to growth of some of the most common fungal types found in contaminated buildings.
Fungal contamination in buildings and the subsequent exposure risk to inhabitants is an increasing concern to builders, property managers and owners, and the property insurance companies that issue coverage. Although the low cost of laboratory analysis has made accurately assessing fungal contamination in residences and buildings possible, the associated costs of remediating fungal-contaminated buildings remains high, due to specialized practices and procedures not found in conventional demolition and construction. In light of this, we sought a method by which the conditions for permissible fungal growth could be detected and corrected before fungal contamination occurs.
Generally, fungi require nutritious media, moisture, and mild temperatures to grow. Although nutritious media and mild temperatures are often found in living and working spaces, relative humidity and surface moisture are often too low for fungal growth to occur. In walls, roof/ceilings, and other plenum spaces however, these contained areas create an environment favorable for mold growth and thus these spaces represent the highest risk for mold development. Indeed, plumbing, appliance, and roof leaks in plenum areas are often causes of loss leading to fungal contamination in a building. The moisture requirement for fungal growth is fulfilled by accidental water incursions, and can be artificially divided into fast, catastrophic leaks and slower leaks. Although catastrophic leaks typically result in the greatest water-related damage, their very nature results in relatively rapid detection and subsequent prevention or remediation of fungal contamination, if handled expeditiously. Slow water incursions such as those related to HVAC system issues, leaking appliances, groundwater incursions, plumbing leaks, and roof leaks are of particular concern, since inhabitants do not always readily detect them.
A small unobtrusive and inexpensive device is needed for detecting these relative humidity levels conducive to fungal growth. In addition, a method and device are needed for analyzing the relative humidity values. Results of the analysis may be reported to a user through a visual and auditory interface or to a host device through a network interface.
In one preferred embodiment of the present invention, a device and method collect and log relative humidity, and evaluate that data using an algorithm to determine if local environmental conditions are conducive for fungal growth of potentially toxicogenic species of Aspergillus, Penicillium, and Stachybotrys, molds commonly found in the United States. The device and method detect conditions conducive to the growth of several species of fungus (mold) and not necessarily mold itself.
In one embodiment, the device, a fungus growth condition meter, is placed in a wall, floor, or ceiling to detect relative humidity levels within the wall or attic cavities. It is particularly useful for placing in xe2x80x9cwetxe2x80x9d walls where plumbing exists and the danger of pipe leakage makes a fungal growth condition more probable. The device may be configured in a cylindrical shape, similar to a hockey puck, or as a rectangular cube suitable for placing in a standard electrical box such as those used to house AC electrical current outlets. A panel on the front of the device indicates growth conditions within the cavity to a user. The interface may comprise a visual alarm, such as a blinking light emitting diode LED, an audio alarm, and a display indicating up to five different levels of relative humidity conditions.
The growth condition meter may include a simple controller with attached memory, a timer, a relative humidity sensor, and the user interface. The timer keeps track of the time of day and creates a fixed time interval at which relative humidity values are sampled using the relative humidity sensor. In addition, the growth condition meter may contain a network interface for connection to a host computer allowing the creation of a network of growth condition meters positioned throughout a building. The host computer may be a standard personal computer, a computer specifically designed for the task of monitoring growth condition meters, or possibly even a personal digital assistant (PDA) or similar device.
In one preferred embodiment, the device executes an algorithm for determining growth conditions within the cavity based on a history of relative humidity samples. To begin the process, the growth condition meter gathers enough samples at a predetermined sampling interval to complete a 24-hour period of aggregate samples. The algorithm then compares the relative humidity samples to predetermined relative humidity thresholds to determine a severity level for fungal growth. The algorithm uses a two-part analysis rubric.
First, the algorithm uses a temporal analysis rubric. This analysis examines all consecutive samples over the previous 24-hour period to determine how long the relative humidity was below a predetermined threshold. Relative humidity levels will typically by cyclical, following a roughly sinusoidal profile over a 24-hour period. By examining consecutive relative humidity values below a fixed threshold, the algorithm can verify that relative humidity is varying in relation to environmental conditions. Additionally, the algorithm verifies that enough time is spent below the threshold creating a thermal burn-off of the humidity to an extent that mold growth is not probable. The threshold level is set to a predetermined programmable value based on environmental conditions where the growth condition meter is installed. Example historical data collected in Houston, Texas indicate that, for the Houston area, a relative humidity threshold of about 80% would be appropriate.
The second rubric is a baseline analysis comparing all samples over the previous 24-hour period to determine if all samples are over a predetermined relative humidity threshold. The baseline rubric allows for a scenario where there is enough time spent below the temporal threshold but the overall relative humidity remains high enough to permit fungal growth. Comparing the relative humidity values to three different threshold levels creates a gradient severity level based on this baseline analysis. The threshold levels are set to predetermined programmable values based on environmental conditions where the growth condition meter is installed. Example historical data collected in the Houston, Texas area indicate that, for the Houston area, appropriate threshold levels are 75%, 70%, and 65%.
The algorithm combines the results of both analysis rubrics to arrive at a composite severity level, which it then reports to the user through the user interface or to a host computer through the network.